Reduction of energy consumption by cellular networks is an important aspect of network evolution both in the context of the “greening” of technology and the significant potential for Operational Expenditures (OPEX) savings. Further, as cellular networks proliferate and increase their nodal density throughout the world, the importance of energy savings becomes compounded.
In addition to the “greening” of technology and OPEX savings, reduced energy consumption may create opportunities for new deployment scenarios. For example, reduced energy consumption may create an opportunity for solar-powered base stations with reasonably sized solar panels in areas with no access to the electrical grid, which is of particular interest for the further spread of mobile broadband services in rural areas, especially in the developing world. Furthermore, on power failure, energy efficiency at a base station (e.g., an enhanced Node B (eNB)) will maximize the base station operating time on power backup.
The collection of base stations in a Radio Access Network (RAN) is one of the largest, if not the largest, consumer of energy in the RAN. The energy requirements of the base stations vary considerably with time of day, day of week, geographic location, etc. A large portion of base stations will experience a low number of connected users and low capacity demand over a significant portion of their service life. Tailoring the energy consumption of the base stations in the RAN to the time dependent capacity demands on the RAN impacts multiple aspects of any approach to energy efficiency from a systems perspective.
The energy consumed by a base station will vary based on the activity of that base station. In particular, the action of transmitting Radio Frequency (RF) energy requires a disproportionately large amount of total energy consumed by the base station, particularly in macro and micro type base stations. Furthermore, owing to the advances in energy efficient processor design, the energy consumed by the processors themselves depends strongly on load. As such, there is a significant energy benefit to keeping any processing load to a minimum.
In a heterogeneous network, there may be many base stations (e.g., pico base stations serving corresponding pico cells) that provide supplemental capacity for the cellular network but do not increase the coverage of the cellular network. More specifically, using a 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) network as an example, the LTE network may include numerous macro eNBs that provide ubiquitous coverage for a desired geographic area and numerous small cell base stations (e.g., pico base stations serving pico cells) that provide supplemental capacity for the cellular network (e.g., hotspots). The eNBs that provide ubiquitous coverage are referred to herein as a coverage eNB layer or simply a coverage layer, whereas the eNBs that provide supplemental capacity are referred to herein as a supporting eNB layer or simply a supporting layer. During times of low demand, some or all of the eNBs in the supporting layer can be turned off or put into a low energy state in order to reduce the energy consumed by the cellular network.
Once the eNBs in the supporting layer are turned off or put into a low energy state, one issue that arises is deciding when the eNBs in the supporting layer should turn back on or transition to an active state. A number of approaches to controlling the operation of eNBs in the supporting layer are outlined in “Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Potential solutions for energy saving for E-UTRAN (Release 11),” 3GPP TR 36.927 V11.0.0, September 2012. Specifically, 3GPP TR 36.927 proposes four approaches. In the first approach, referred to as an “OAM predefined low-load periods policy,” a coverage cell uses a proprietary algorithm that relies on predefined low load periods for each neighbor hotspot cell to decide which hotspot cells should be activated when the coverage cell detects high load. In a second approach, referred to as an “IoT measurement” approach, the coverage cell requests some dormant hotspot cells to switch on their listening capacity to perform and report Interference over Thermal (IoT) measurements when the coverage cell detects high load. In a third approach, referred to as a “UE measurement” approach, the coverage cell requests some dormant hotspot cells to transmit pilot signals for at least a short amount of time when the coverage cell detects high load. The User Equipment devices (UEs) covered by the coverage cell are configured to perform measurements on the pilot signals and send corresponding feedback to the coverage cell. Based on the measurements, the coverage cell determines which hotspot cells should be switched on. In a fourth approach, referred to as a “Positioning information” approach, the coverage cell uses a combination of UE locations, cell locations, and cell radii/transmit powers to decide which hotspots should be switched on when the coverage cell detects high load.
Each of the approaches described in 3GPP TR 36.927 has limitations. The Operations, Administration, Maintenance (OAM) based approach is coarse and does not dynamically adapt to the short term fluctuations in load. The UE measurement approach requires dormant cells to activate in a hunt and guess type of approach. The Positioning information approach requires relatively accurate position information for UEs in the vicinity. The IoT approach has the advantage of being based on passive sensing and is capable of responding to the dynamic changes in the environment. However, the IoT only provides a gross indication of uplink activity and does not provide a strong localization assessment. As well, as described in 3GPP TR 36.927, there is no opportunity for joint decision making between eNBs based on their IoT observations.
In light of the discussion above, there is a need for improved systems and methods for autonomously activating or waking base stations (e.g., eNBs) in a supporting layer of a RAN.